JoBert

JoBert is a text classifier designed to analyze job offer paragraph texts and categorize each one into predefined 5 classes.

Please refer to this repository when using the model.

• Developed by: AhmedBou
• License: apache-2.0

Classes:

• About the Company
• Job Description
• Job Requirements
• Responsibilities
• Benefits
• Other

0. About the Company:
   Details about the hiring company, including its values, mission, and culture.

1. Job Description:
   General information about the role, the tasks involved, and the purpose of the job.

2. Job Requirements:
   Skills, qualifications, and experience needed for the job.

3. Responsibilities:
   Specific tasks and duties associated with the role.

4. Benefits:
   Information about the perks, benefits, and compensation offered.

5. Other:
   Additional information that doesn't fit into the above categories.

Load the Model for Inference:

import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("AhmedBou/JoBert")
model = AutoModelForSequenceClassification.from_pretrained("AhmedBou/JoBert")


label_names = ['About the Company', 'Job Description', 'Job Requirements', 'Responsibilities', 'Benefits', 'Other']
inference_model = model

text_snippet = "you must know how to use Python, Java, and SQL, and you should have 3 years of experience"
inference_inputs = tokenizer(text_snippet, return_tensors='pt')
inference_inputs = {key: val for key, val in inference_inputs.items()}
inference_outputs = inference_model(**inference_inputs)
inference_logits = inference_outputs.logits
inference_prediction = torch.argmax(inference_logits).item()
inference_label_name = label_names[inference_prediction]

print(f"Inference Result: Predicted Label - {inference_label_name}")